HIV-1 Nucleotide Sequence Comprehensive Analysis: A Computational Approach

Title:HIV-1 Nucleotide Sequence Comprehensive Analysis: A Computational Approach

Volume: 12 Issue: 4

Author(s): José Irahe Kasprzykowski*, Kiyoshi Ferreira Fukutani, Helton Fábio, Aldina Maria Prado Barral and Artur Trancoso Lopo de Queiroz

Affiliation:

• Applied Computing Post Graduate Program, Feira de Santana State University, Feira de Santana-BA,Brazil

Keywords: Comparative genomics, sequence subtyping, HIV, sequence mapping, bioinformatics, genomics, highly mutant virus.

Abstract: Background: Acquired Immunodeficiency Syndrome (AIDS) is a large-scale pandemic caused by the infection of Human Immunodeficiency Virus (HIV). This virus infects over 40 million people worldwide. In the search for pandemic control, many drug resistance tests have been performed, resulting in the generation of large genomic data amount. These data are stored in biological databases, increasing on a daily basis. However, the majority of genomic data lacks important information, regarding virus subtype distribution, in the primary databases, e.g. GenBank.

Objective: A novel software tool to obtain, index and analyze highly mutational virus data, such as all HIV-1 sequence data from GenBank.

Method: The software aligns all sequences containing a complete genome (HXB2) for mapping purposes. In addition, all sequences with subtype references are locally aligned to classify all data into genotypic niches.

Results: Our results detail the prevalence of every subtype from a global HIV-1 sequence perspective, highlighting increases in the number of sequences related to recombinant subtypes. We were also able to identify country-based distribution of sequences according to geographical data distribution. All data were analyzed on a reasonable timescale, particularly in comparison to classic methods.

Conclusion: Our software represents an important contribution to HIV molecular epidemiology and offers a technique to rapidly classify new sequences, in addition to providing insight about sequence coverage density, subtype and country distribution. This data, together with cross-referencing, will aid in the generation of a novel, comprehensive and updated HIV-1 database.

Cite this article as:

Irahe Kasprzykowski José*, Ferreira Fukutani Kiyoshi, Fábio Helton, Maria Prado Barral Aldina and Trancoso Lopo de Queiroz Artur, HIV-1 Nucleotide Sequence Comprehensive Analysis: A Computational Approach, Current Bioinformatics 2017; 12 (4) . https://dx.doi.org/10.2174/1574893611666161027142611

DOI https://dx.doi.org/10.2174/1574893611666161027142611	Print ISSN 1574-8936
Publisher Name Bentham Science Publisher	Online ISSN 2212-392X